Solving differential‐algebraic equations in power system dynamic analysis with quantum computing
Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, wh...
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | Energy Conversion and Economics |
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| Online Access: | https://doi.org/10.1049/enc2.12107 |
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| author | Huynh T. T. Tran Hieu T. Nguyen Long T. Vu Samuel T. Ojetola |
| author_facet | Huynh T. T. Tran Hieu T. Nguyen Long T. Vu Samuel T. Ojetola |
| author_sort | Huynh T. T. Tran |
| collection | DOAJ |
| description | Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications. |
| format | Article |
| id | doaj-art-7b6d9eba4de24ba5b08a141b06168dc7 |
| institution | DOAJ |
| issn | 2634-1581 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Conversion and Economics |
| spelling | doaj-art-7b6d9eba4de24ba5b08a141b06168dc72025-08-20T02:47:49ZengWileyEnergy Conversion and Economics2634-15812024-02-0151284110.1049/enc2.12107Solving differential‐algebraic equations in power system dynamic analysis with quantum computingHuynh T. T. Tran0Hieu T. Nguyen1Long T. Vu2Samuel T. Ojetola3Department of Electrical & Computer Engineering North Carolina Agricultural and Technical State University Greensboro USADepartment of Electrical & Computer Engineering North Carolina Agricultural and Technical State University Greensboro USAThe Energy & Environment Directorate Pacific Northwest National Laboratories Richland USAThe Electric Power System Research Sandia National Laboratories Albuquerque USAAbstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.https://doi.org/10.1049/enc2.12107computational complexitydistributed energy resourcespower system dynamicsquantum computing |
| spellingShingle | Huynh T. T. Tran Hieu T. Nguyen Long T. Vu Samuel T. Ojetola Solving differential‐algebraic equations in power system dynamic analysis with quantum computing Energy Conversion and Economics computational complexity distributed energy resources power system dynamics quantum computing |
| title | Solving differential‐algebraic equations in power system dynamic analysis with quantum computing |
| title_full | Solving differential‐algebraic equations in power system dynamic analysis with quantum computing |
| title_fullStr | Solving differential‐algebraic equations in power system dynamic analysis with quantum computing |
| title_full_unstemmed | Solving differential‐algebraic equations in power system dynamic analysis with quantum computing |
| title_short | Solving differential‐algebraic equations in power system dynamic analysis with quantum computing |
| title_sort | solving differential algebraic equations in power system dynamic analysis with quantum computing |
| topic | computational complexity distributed energy resources power system dynamics quantum computing |
| url | https://doi.org/10.1049/enc2.12107 |
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